Monarchs navigate to their overwintering sites with striking precision each year. We are combining novel miniaturized tracking electronics and machine learning methods with next-generation sequencing and imaging approaches to understand how they do this.
This work is an ongoing collaboration with David Blaauw (UM EECS), Hun-Seok Kim (UM EECS) and Inhee Lee (U Pittsburgh ECE). We are grateful for support from the Monarch Butterfly Fund Flight Challenge and National Geographic Society.
Join our tracking project!
Over 100 volunteers from across the US and Canada are active collaborators on this project, helping us collect data to refine our tracking methods. Learn how to become a part of the team!
Image credit: Katherine Ernst
Recent Publications
- Lee I∗, Hsiao R, Carichner G, Hsu C-W, Yang M, Shoouri S, Ernst K, Carichner T, Li Y, Lim J, Julick CR, Moon E, Sun Y, Phillips J, Montooth KL, Green II DA, Kim H-S, and Blaauw D (2021) mSAIL: Milligram-Scale Multi-Modal Sensor & Analytics Monitoring Platform for Monarch Butterfly Migration Tracking. (Accepted for the 2021 ACM Annual International Conference on Mobile Computing and Networking)
- Yang M, Hsiao R, Carichner G, Ernst K, Lim J, Green II, DA, Lee I, Blaauw D, AND Kim H-S. Migrating Monarch Butterfly Localization Using Multi-Sensor Fusion Neural Networks. Submitted 14 Dec 2019. arXiv:1912.06907.